AI is moving beyond experiments—time to scale
Companies around the world are shifting from small-scale pilots to fully integrating AI into their core operations. This is happening fast, and for a good reason—costs are coming down, and success rates are going up. Businesses that once hesitated to invest in AI due to uncertainty are now seeing clear returns. Those who don’t scale now will be forced to catch up later.
The real change is in how AI is being deployed. Companies are focusing on transformational AI—projects that redefine how businesses operate. This is about automating what was once manual, predicting outcomes more accurately, and restructuring entire workflows to maximize speed and intelligence.
According to the Infosys AI Business Value Radar, which analyzed 3,240 organizations across 132 AI use cases, 19% of AI projects fully achieve their business goals, and 32% partially meet them. It’s happening in real-world deployments. Companies with a clear strategy and the right infrastructure are seeing AI deliver measurable results, while others are still stuck in trial mode.
“For executives, the message is clear: AI at scale is no longer a luxury—it’s a necessity. The companies that invest in AI today, build strong data systems, and align AI with their core business models are the ones that will dominate tomorrow.”
AI success isn’t equal across industries
AI isn’t delivering the same results everywhere. Some industries are leading with strong returns, while others struggle to implement AI effectively. The difference comes down to digital maturity, regulatory constraints, and technical infrastructure. Businesses operating in professional services, life sciences, high tech, telecommunications, and insurance are seeing higher AI success rates. These industries already have structured data, well-defined AI applications, and leadership committed to scaling AI investments.
On the other hand, industries like travel, hospitality, manufacturing, retail, and the public sector face more hurdles. Many of these businesses rely on legacy systems, making AI integration more complex. AI thrives on high-quality data, and in industries where data is fragmented or outdated, AI projects take longer to deliver meaningful impact. Without addressing these foundational issues, AI adoption remains inefficient, slowing down progress.
The financial services sector, despite being a white-collar industry, ranks below average in AI effectiveness. The main blockers? Strict regulatory requirements and outdated data systems that need modernization before AI can be fully leveraged. Compliance concerns often slow AI adoption, forcing financial firms to take a more cautious approach, even when the potential upside is clear.
Executives need to recognize where their industry stands in AI adoption. If AI success is already evident in their sector, it’s a matter of scaling efficiently. If their industry is struggling, the priority should be fixing underlying technical and regulatory challenges. The companies that solve these problems first will take the lead, leaving the rest to play catch-up.
IT, operations, and cybersecurity are leading AI adoption
The areas seeing the fastest and most effective AI integration are IT, operations, and facilities. These functions are critical to a company’s ability to improve efficiency and maintain competitive performance, which is why businesses are prioritizing AI adoption in these areas. According to the Infosys study, 38% of organizations have deployed AI in IT and operations, making it the most widely adopted category.
Cybersecurity, resilience, and software development are also gaining traction, with 30% of organizations implementing AI in these areas. These functions benefit from automation and predictive analysis, allowing companies to proactively address security threats, optimize workflows, and accelerate product development. AI in cybersecurity is particularly valuable, as businesses face an increasing volume of sophisticated cyber threats that require advanced detection and response capabilities.
AI is also being deployed in customer-facing areas like marketing, customer service, and sales, but with lower overall adoption rates. Meanwhile, industry-specific AI applications—like claims processing in insurance and clinical trials in life sciences—are transforming specialized workflows. These AI-driven changes require significant restructuring of data and technology infrastructure, making them more complex but also more impactful when executed correctly.
Executives should focus their AI strategies on areas where adoption is already proving successful. IT, operations, and cybersecurity are clear priorities, delivering measurable returns and higher success probabilities.
“Companies investing in these functions are improving processes while building stronger, more resilient operations that can scale with AI technology.”
Employee training and change management are critical for AI success
Successful AI deployment depends on how well employees adapt to and work alongside AI systems. Yet, most companies are neglecting this factor. According to the Infosys AI Business Value Radar, only 16% of organizations have implemented structured change management and employee training programs for AI. This is a major gap, and it’s preventing companies from getting the full value out of their AI investments.
Businesses that invest in employee readiness see much better results. When organizations prioritize AI education and structured transition plans, AI deployment success rates improve dramatically—by up to 18 percentage points. The reason is simple: employees who understand AI and know how to use it effectively make AI adoption smoother, reducing resistance and increasing productivity. Without this, even the most advanced AI systems can struggle to deliver value.
Executives need to take this seriously. Companies that fail to train their workforce properly will face internal pushback, delayed adoption, and wasted investment. Those that integrate AI training throughout the company, ensuring teams at all levels understand and utilize the technology, will move faster and see stronger business outcomes. AI works best when people know how to integrate it into their daily work, making training and change management a strategic priority, not an afterthought.
AI needs a structured operating model and governance to scale
Scaling AI requires a structured operating model that aligns with business goals. Without a clear framework, AI initiatives become fragmented, failing to drive significant impact. Companies that succeed treat AI as a core part of their business strategy. They design AI operating models that ensure long-term scalability, risk management, and integration across all functions.
The Infosys report highlights “agentic AI” as a key driver of enterprise transformation. This approach redefines operating models by embedding AI deeper into business processes, allowing for faster decision-making, automation, and process optimization. Businesses that embrace this shift position themselves ahead of the competition, leveraging AI for fundamental business transformation.
Governance is just as critical. As AI becomes more embedded in decision-making, companies must establish dedicated AI governance task forces to enforce accountability, manage risks, and address ethical considerations. Without proper oversight, AI deployments can lead to inconsistent results, compliance risks, and operational failures. Leaders who take AI governance seriously make sure AI investments remain aligned with company objectives while minimizing unintended consequences.
Satish H C, Executive Vice President and Chief Delivery Officer at Infosys, emphasizes this point: “Enterprise AI is ready to scale. With effective use of data architecture, operating models, and employee readiness, businesses can accelerate their adoption of AI to achieve measurable success.”
Jeff Kavanaugh, Head of Infosys Knowledge Institute, also states that the companies achieving real AI success are those that “go beyond experimentation and fundamentally change their operating model, as well as support their employees through the journey.”
Executives should act now to build structured AI frameworks that ensure scalability, risk management, and full integration into business operations.
Key executive takeaways
- AI scaling is no longer optional: Businesses are moving beyond AI experimentation as costs drop and success rates improve. Leaders should prioritize scaling transformational AI use cases to drive measurable impact.
- Industry AI success depends on readiness: Sectors with structured data and mature digital ecosystems, like life sciences and telecommunications, are seeing stronger AI returns. Companies in lagging industries must address legacy systems and regulatory barriers to stay competitive.
- AI investments should focus on proven use cases: IT, operations, and cybersecurity lead in AI adoption, delivering higher success rates. Decision-makers should allocate resources to these areas while integrating AI into specialized workflows for greater long-term efficiency.
- Workforce readiness determines AI success: Only 16% of companies have structured AI training and change management programs, yet these efforts significantly boost success rates. Executives should invest in upskilling employees to reduce resistance and maximize AI adoption.
- Governance and structured AI models are essential: Without a clear operating model and governance, AI adoption remains fragmented. Leaders must implement structured AI frameworks with risk management strategies to scale effectively and ensure accountability.
